Improve Data Availability Research Articles

A Differential Privacy Protection Protocol Based on Location Entropy

A Location-Based Service (LBS) refers to geolocation-based services that bring both convenience and vulnerability. With an increase in the scale and value of data, most existing location privacy protection protocols cannot balance privacy and utility. To solve the revealing problems in LBS, we propose a differential privacy protection protocol based on location entropy. First, we design an algorithm of the best-assisted user selection for constructing anonymity sets. Second, we employ smart contracts to evaluate the credibility of participants, which ensures the honesty of participants. Moreover, we provide a comprehensive experiment; the theoretical analysis and experiments show that the proposed protocol effectively resists background knowledge attacks. Generally, our protocol improves data availability. Particularly, it realizes user-controllable privacy protection, which improves privacy protection and strengthens security.

Lethal use of force surveillance: practical considerations for open-source database linkage

Currently, there remains no reliable and timely government tracking in the USA of deaths caused by law enforcement. Federal efforts to track these events are insufficient, generally missing as many...

Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach

The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.

Optimizing the Performance of Data Warehouse by Query Cache Mechanism in Big Data

In today's world of Business Intelligence (BI), fast and efficient access to data from Data Warehouses (DW) is crucial. With the increasing amount of Big Data, caching has become one of the most effective techniques for improving data access performance. DWs are widely used by organizations for managing and using data in Decision Support Systems (DSS). To optimize the performance of fetching data from DWs, various methods have been employed, and one of them is the Query Cache method. Our proposed work focuses on a cache-based mechanism that improves the performance of DWs in two ways. First, it reduces the execution time by directly accessing records from cache memory, and second, it saves cache memory space by eliminating non-frequently used data. Our goal is to fill the cache memory with the most frequently used data. To achieve this objective, we utilize an aging-based Least Frequently Used (LFU) algorithm that considers the size and frequency of data simultaneously. This algorithm manages the priority and expiry age of the data in cache memory by taking into account both the size and frequency of data. LFU assigns priorities and counts the age of data placed in the cache memory. The cache block entry with the lowest age count and priority is eliminated first. Ultimately, our proposed cache mechanism efficiently utilizes cache memory and significantly improves the performance of data access between the main DW and the business user query

Data visualization explorer: A tool for participant representation in pivotal trials of FDA-approved medicinal products

This paper presents a 2015-2021US Food and Drug Administration (FDA) Drug Trials Snapshots (DTS) Data Visualization Explorer-an interactive data visualization web-based tool (https://arielcarmeli.shinyapps.io/fda-drug-trial-snapshots-data-explorer) built in R and based on publicly available FDA clinical trial participation data and disease incidence data from the National Cancer Institute and Centers for Disease Control and Prevention. FDA data can be explored by race, ethnicity, sex, age group, therapeutic area, pharmaceutical sponsor, and approval year for clinical trials that supported each of the 339 FDA drug and biologic approvals between 2015 and 2021. This work provides several advantages relative to past literature and DTS reports: a dynamic data visualization tool; race, ethnicity, sex, and age group data presented in one place; data on sponsor; and emphasis on data distributions instead of averages. We present recommendations for improved data access, reporting, and communication to assist leaders in making evidence-based decisions to improve trial representation to enhance health equity.

Accuracy Assessment and Validation of Multi-Source CHIRPS Precipitation Estimates for Water Resource Management in the Barada Basin, Syria

The lack of sufficient precipitation data has been a common problem for water resource planning in many arid and semi-arid regions with sparse and limited weather monitoring networks. Satellite-based precipitation products are often used in these regions to improve data availability. This research presents the first validation study in Syria for Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates using in-situ precipitation data. The validation was performed using accuracy and categorical statistics in the semi-arid Barada Basin, Syria, between 2000 and 2020. Multiple temporal scales (daily, pentad, monthly, seasonally, and annual) were utilized to investigate the accuracy of CHIRPS estimates. The CHIRPS results indicated advantages and disadvantages. The main promising result was achieved at the seasonal scale. Implementing CHIRPS for seasonal drought was proven to be suitable for the Barada Basin. Low bias (PBwinter = 2.1%, PBwet season = 12.7%), high correlation (rwet season = 0.79), and small error (ME = 4.25 mm/winter) support the implementation of CHIRPS in winter and wet seasons for seasonal drought monitoring. However, it was observed that CHIRPS exhibited poor performance (inland pentads) in reproducing precipitation amounts at finer temporal scales (pentad and daily). Underestimation of precipitation event amounts was evident in all accuracy statistics results, and the magnitude of error was higher with more intense events. CHIRPS results better corresponded in wet months than dry months. Additionally, the results showed that CHIRPS had poor detection skill in drylands; on average, only 20% of all in-situ precipitation events were correctly detected by CHIRPS with no effect of topography found on detection skill performance. This research could be valuable for decision-makers in dryland regions (as well as the Barada Basin) for water resource planning and drought early warning systems using CHIRPS.

Monitoring theSustainable Development Goalsin cities: Potentials and pitfalls of using smart city data

Smart city strategies highlight the potential to generate new type of data through new technology, for example crowdsourced data. Based on an empirical study, we show the potentials and limits of using new data for monitoring urban sustainability and especially theSustainable Development Goals.The latest debate on smart cities and sustainability is underpinned by the United Nations’2030 Agendaand their accompanyingSustainable Development Goals (SDGs),which place urban data and monitoring systems at the forefront. Therefore, there is a strong need to assess the data-driven capabilities that will help achieve theSDGs. To fill the capability gaps between existing tools andSDGindicators, new smart city data sources are now available. However, scant indicators and assessment criteria have been empirically validated. This paper identifies some of the challenges alongside the potential of using new local data in urban monitoring systems. A case study of anSDGmonitoring platform implementation in a district of Berlin is examined, and the results show that the use of locale-specific, and unofficial data not only improves data availability, but it also encourages local public participation. Based on our empirical findings, we determine that the incorporation of new data for urban sustainability monitoring should be treated as a complex social process.

National-scale geodatabase of catchment characteristics in the Philippines for river management applications.

Quantitative descriptions of stream network and river catchment characteristics provide valuable context for enabling geomorphologically-informed sustainable river management. For countries where high-quality topographic data are available, there are opportunities to enable open access availability of baseline products from systematic assessment of morphometric and topographic characteristics. In this study, we present a national-scale assessment of fundamental topographic characteristics of Philippine river systems. We applied a consistent workflow using TopoToolbox V2 to delineate stream networks and river catchments using a nationwide digital elevation model (DEM) acquired in 2013 and generated through airborne Interferometric Synthetic Aperture Radar (IfSAR). We assessed morphometric and topographic characteristics for 128 medium- to large-sized catchments (catchment area > 250 km2) and organised the results in a national-scale geodatabase. The dataset realises the potential of topographic data as part of river management applications, by enabling variations in hydromorphology to be characterised and contextualised. The dataset is used to reveal the diversity of stream networks and river catchments in the Philippines. Catchments have a continuum of shapes (Gravelius compactness coefficient ranges from 1.05 to 3.29) with drainage densities that range from 0.65 to 1.23 km/km2. Average catchment slope ranges from 3.1 to 28.1° and average stream slope varies by more than an order of magnitude from 0.004 to 0.107 m/m. Inter-catchment analyses show the distinctive topographic signatures of adjacent river catchments; examples from NW Luzon highlight topographic similarity between catchments whereas examples from Panay Island shown marked topographic differences. These contrasts underline the importance of using place-based analyses for sustainable river management applications. By designing an interactive ArcGIS web-application to display the national-scale geodatabase, we improve data accessibility and enable users to freely access, explore and download the data (https://glasgow-uni.maps.arcgis.com/apps/webappviewer/index.html?id=a88b9ca0919f4400881eab4a26370cee). The national-scale geodatabase provides a baseline understanding of fundamental topographic characteristics in support of varied geomorphological, hydrological and geohazard susceptibility applications.

Rural Public Health Data Challenges During the COVID-19 Pandemic: The Case for Building Better Systems Ahead of a Public Health Crisis.

Rural public health personnel serve communities that have been particularly susceptible to COVID-19 and yet faced the pandemic with far less well-resourced capacity than their urban counterparts. A critical aspect of addressing local health inequities is access to high-quality population data and the capacity to effectively use data to support decision making. However, much of the data required to investigate inequities are not readily available to rural local health departments and the tools and training to analyze data are often lacking. The purpose of our effort was to explore rural data challenges related to COVID-19 and provide recommendations for improving rural data access and capacity ahead of future crises. We gathered qualitative data in 2 phases, more than 8 months apart, from rural public health practice personnel. Initial data were gathered in October-November 2020 regarding rural public health data needs during the COVID-19 pandemic and then to later identify whether the same findings held true in July 2021 or whether access to and capacity to use data to address the pandemic and related inequities improved as the pandemic progressed. In our 4-state exploration focused on access and use of data among rural public health systems to promote health equity in the Northwest United States, we found tremendous and ongoing unmet data needs, challenges with communicating data, and a lack of capacity to meet this public health crisis. Recommendations for addressing these challenges include increasing dedicated resources specifically to rural public health systems, improving data access and infrastructure, and providing dedicated data-related workforce development.

Quality Management System for an IoT Meteorological Sensor Network—Application to Smart Seoul Data of Things (S-DoT)

Meteorological data with a high horizontal resolution are essential for user-specific weather application services, such as flash floods, heat waves, strong winds, and road ice, in urban areas. National meteorological observation networks, such as the Automated Synoptic Observing System (ASOS) and Automated Weather System (AWS), provide accurate but low horizontal resolution data to address urban-scale weather phenomena. Many megacities are constructing their own Internet of Things (IoT) sensor networks to overcome this limitation. This study investigated the status of the smart Seoul data of things (S-DoT) network and the spatial distribution of temperature on heatwave and coldwave event days. The temperature at above 90% of S-DoT stations was higher than that at the ASOS station, mainly because of different surface covers and surrounding local climate zones. A quality management system for an S-DoT meteorological sensor network (QMS-SDM) comprising pre-processing, basic quality control, extended quality control, and data reconstruction using spatial gap-filling was developed. The upper threshold temperatures for the climate range test were set higher than those adopted by the ASOS. A 10-digit flag for each data point was defined to discriminate between normal, doubtful, and erroneous data. Missing data at a single station were imputed using the Stineman method, and the data with spatial outliers were filled with values at three stations within 2 km. Using QMS-SDM, irregular and diverse data formats were changed to regular and unit-format data. QMS-SDM application increased the amount of available data by 20-30%, and significantly improved data availability for urban meteorological information services.

Data sharing and reuse practices: disciplinary differences and improvements needed

PurposeThis study investigates differences and commonalities in data production, sharing and reuse across the widest range of disciplines yet and identifies types of improvements needed to promote data sharing and reuse.Design/methodology/approachThe first authors of randomly selected publications from 2018 to 2019 in 20 Scopus disciplines were surveyed for their beliefs and experiences about data sharing and reuse.FindingsFrom the 3,257 survey responses, data sharing and reuse are still increasing but not ubiquitous in any subject area and are more common among experienced researchers. Researchers with previous data reuse experience were more likely to share data than others. Types of data produced and systematic online data sharing varied substantially between subject areas. Although the use of institutional and journal-supported repositories for sharing data is increasing, personal websites are still frequently used. Combining multiple existing datasets to answer new research questions was the most common use. Proper documentation, openness and information on the usability of data continue to be important when searching for existing datasets. However, researchers in most disciplines struggled to find datasets to reuse. Researchers' feedback suggested 23 recommendations to promote data sharing and reuse, including improved data access and usability, formal data citations, new search features and cultural and policy-related disciplinary changes to increase awareness and acceptance.Originality/valueThis study is the first to explore data sharing and reuse practices across the full range of academic discipline types. It expands and updates previous data sharing surveys and suggests new areas of improvement in terms of policy, guidance and training programs.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2021-0423.

GraphQL: A Systematic Mapping Study

GraphQL is a query language and execution engine for web application programming interfaces (APIs) proposed as an alternative to improve data access problems and versioning of representational state transfer APIs. In this article, we thoroughly study the GraphQL field, first describing the GraphQL paradigm and its conceptual framework, and then conducting a systematic mapping study of 84 primary studies selected from an original set of 3,185. Our work analyzes trends or knowledge gaps about GraphQL by general classification of the studies and specific classification of this research topic. The study’s main conclusions show that GraphQL adoption is growing in the community as a strong alternative to implement APIs. However, we identified the need to strengthen the amount and rigor of empirical evidence collection in applied industry and government studies. In addition, we revealed the opportunity for specific studies on most GraphQL components, especially the consumption of GraphQL API services.

Challenges of Documenting Historic Water Systems

ABSTRACT Geospatial research in archaeology often relies on datasets previously collected by other archaeologists or third-party groups, such as state or federal government entities. This article discusses our work with geospatial datasets for identifying, documenting, and evaluating prehistoric and historic water features in the western United States. As part of a project on water heritage and long-term views on water management, our research has involved aggregating spatial data from an array of open access and semi-open access sources. Here, we consider the challenges of working with such datasets, including outdated or disorganized information, and fragmentary data. Based on our experiences, we recommend best practices: (1) locating relevant data and creating a data organization method for working with spatial data, (2) addressing data integrity, (3) integrating datasets in systematic ways across research cohorts, and (4) improving data accessibility.

The AI algorithm for text encryption using Steganography

The human being sought to find a human variety of techniques to assure data access with complete confidentiality. The transition from the use of. regular text and audio data to digital media has improved data access and transport. It has become relatively simple to intercept data sent across networks or get access to a variety of machines. Steganography is the study of concealing the existence of communication by embedding hidden data in multimedia payloads such as text, image, audio, and video. This project investigates how to improve steganography by merging text and image production to provide invisible encryption, security, and robustness in digital images. Text hide into another text using quality in the proposed system include Mean Square Error (MSE), Normalized Correlation (NC), and Normalized Cross–correlation Mean Squared Error, Histogram Analysis, Standard Deviation, and Statistical Test and Analysis. The algorithm of our suggested system seems to meet the greatest standards of security, perceptions, and capability. Using the standard of ASCII Control characters for a cover word, the procedure for using the processed system is supplied from the cover sentence. The same method is used for other words from different cover sentences, given the line numbers of each cover sentence and Stego sentence. Keywords: Algorithm, Secret Text, Steganography,

U-net-based semantic classification for flood extent extraction using SAR imagery and GEE platform: A case study for 2019 central US flooding

Data-driven models for water body extraction have experienced accelerated growth in recent years, thanks to advances in processing techniques and computational resources, as well as improved data availability. In this study, we modified the standard U-Net, a convolutional neural network (CNN) method, to extract water bodies from scenes captured from Sentinel-1 satellites of selected areas during the 2019 Central US flooding. We compared the results to several benchmark models, including the standard U-Net and ResNet50, an advanced thresholding method, Bmax Otsu, and a recently introduced flood inundation map archive. Then, we looked at how data input types, input resolution, and using pre-trained weights affect the model performance. We adopted a three-category classification frame to test whether and how permanent water and flood pixels behave differently. Most of the data in this study were gathered and pre-processed utilizing the open access Google Earth Engine (GEE) cloud platform. According to the results, the adjusted U-Net outperformed all other benchmark models and datasets. Adding a slope layer enhances model performance with the 30 m input data compared to training the model on only VV and VH bands of SAR images. Adding DEM and Height Above Nearest Drainage (HAND) model data layer improved performance for models trained on 10 m datasets. The results also suggested that CNN-based semantic segmentation may fail to correctly classify pixels around narrow river channels. Furthermore, our findings revealed that it is necessary to differentiate permanent water and flood pixels because they behave differently. Finally, the results indicated that using pre-trained weights from a coarse dataset can significantly minimize initial training loss on finer datasets and speed up convergence.

Programming parallel dense matrix factorizations and inversion for new-generation NUMA architectures

We propose a methodology to address the programmability issues derived from the emergence of new-generation shared-memory NUMA architectures. For this purpose, we employ dense matrix factorizations and matrix inversion (DMFI) as a use case, and we target two modern architectures (AMD Rome and Huawei Kunpeng 920) that exhibit configurable NUMA topologies. Our methodology pursues performance portability across different NUMA configurations by proposing multi-domain implementations for DMFI plus a hybrid task- and loop-level parallelization that configures multi-threaded executions to fix core-to-data binding, exploiting locality at the expense of minor code modifications. In addition, we introduce a generalization of the multi-domain implementations for DMFI that offers support for virtually any NUMA topology in present and future architectures.Our experimentation on the two target architectures for three representative dense linear algebra operations validates the proposal, reveals insights on the necessity of adapting both the codes and their execution to improve data access locality, and reports performance across architectures and inter- and intra-socket NUMA configurations competitive with state-of-the-art message-passing implementations, maintaining the ease of development usually associated with shared-memory programming.

Presentation of DeMa (Decision Support Software and Database for Wellfield Management) and Its Application for the Wadi Al Arab Wellfield

This article aims to present the structure and the workflow of a new software DeMa (Decision Support Software and Database for Wellfield Management), to support wellfield managers in their decision-making processes. There is a recognized need to improve the management of groundwater resources, especially with the increased demand for fresh water in arid and semi-arid regions. DeMa differentiates from other available software, by combining data collected for the well’s maintenance, operation, design, installations, and cost data with the collected hydrological field measurements. Additionally, DeMa links the different information and provides an effective graphical representation of the data. We applied the software to the Wadi Al Arab wellfield case study to support wellfield managers in the decision-making process of three typical problems: identification of missing data and information concerning the wells, identification of maintenance needs for a well, and identification of a suitable location for a new well. In the application to the Wadi Al Arab wellfield (Jordan), we collected data and documents from the Yarmouk Water Company (YWC), the Jordan Ministry of Water and Irrigation (MWI), and private drilling companies. The software application highlights the beneficial effects of the digitalization of water resources management by improving data availability and management and achieving data and research-based decisions on the wellfield.

An Effective Management Model for Data Caching in MANET Environment

A mobile ad-hoc (MANET) network has the main challenge to provide the needed data for the desired mobile nodes. An efficient on request routing protocol for MANET is Ad-hoc on-demand Distance Vector (AODV), which is based on two main methods: route discovery and route maintenance. Route discovery is the process used to detect a route to the destination from the packet source, while route maintenance is the process used to detect a link failure and repair it. Cooperative caching tends improving data availability in mobile ad-hoc networks, the coordination of cache discovery and cache management strategies is very significant in the cooperative caching of MANETs because requests for data and answers to requested data can be reduced simply due to interference, network congestion, or when a forwarding node is out of reach and the route breaks down. Cooperative cache management is much more complicated in cooperative caching because it also depends on neighbouring nodes to decide what to cache. In this paper, three algorithms were proposed: (1) a combination algorithm for cache admission control based on cache data and location of data to save space and reduce data redundancy, (2) a value-based policy for cache placement and replacement instead of the more common least recently used strategy, depending on metrics that describe cached items to increase the local cache hit ratio, and (3) a combined algorithm for cache consistency that includes time-to-live, pull, and push policies to enhance data availability and system scalability. The proposed algorithm implemented by the NS3 simulation program; which used to create a network using the AODV protocol in several parameters and achieve better system performance.

An Effective Management Model for Data Caching in MANET Environment

A mobile ad-hoc (MANET) network has the main challenge to provide the needed data for the desired mobile nodes. An efficient on request routing protocol for MANET is Ad-hoc on-demand Distance Vector (AODV), which is based on two main methods: route discovery and route maintenance. Route discovery is the process used to detect a route to the destination from the packet source, while route maintenance is the process used to detect a link failure and repair it. Cooperative caching tends improving data availability in mobile ad-hoc networks, the coordination of cache discovery and cache management strategies is very significant in the cooperative caching of MANETs because requests for data and answers to requested data can be reduced simply due to interference, network congestion, or when a forwarding node is out of reach and the route breaks down. Cooperative cache management is much more complicated in cooperative caching because it also depends on neighbouring nodes to decide what to cache. In this paper, three algorithms were proposed: (1) a combination algorithm for cache admission control based on cache data and location of data to save space and reduce data redundancy, (2) a value-based policy for cache placement and replacement instead of the more common least recently used strategy, depending on metrics that describe cached items to increase the local cache hit ratio, and (3) a combined algorithm for cache consistency that includes time-to-live, pull, and push policies to enhance data availability and system scalability. The proposed algorithm implemented by the NS3 simulation program; which used to create a network using the AODV protocol in several parameters and achieve better system performance.

The Measurement and Analysis of E-Commerce: Frameworks for Improving Data Availability

Interest in the expansion of e-commerce as a driver for economic development, especially ecommerce across borders, has intensified in recent years. While there is an apparent widespread consensus on the environmental factors necessary to promote e-commerce in developing countries, the basis of empirical evidence on both the extent of e-commerce and the extent to which this is associated with any of its drivers is disappointingly weak. This paper aims at summarizing the current state of affairs with respect to the availability of data and suggests some ways forward in terms of generating an increased supply of policy-relevant data and analysis.